Image-Based Vehicle Localization with Precomputed Embedding Retrieval
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately updating their localized state due to sensor outages or algorithm failures, leading to inefficiencies and potential loss of positional tracking.
Innovation Solution
A localization system that utilizes pre-computed image embeddings and feature representations to determine the current location of an autonomous vehicle by comparing query images with a database of pre-computed embeddings, reducing computational costs through selective retrieval and regression-based displacement calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle continuously updates its localized state using sensor data and algorithms, then the localization accuracy is maintained, but the computational resources and processing time increase significantly
Solution Approach 1:
The system pre-computes image embeddings and stores them in a database before they are needed for localization. When localization is required, the system retrieves pre-computed embeddings instead of computing them in real-time, significantly reducing computational resources and energy consumption while maintaining localization accuracy
Solution Approach 2:
The system creates compressed representations (embeddings) of images that capture essential visual features while occupying minimal storage space. These embeddings are stored in a database and used for comparison during localization, reducing the computational burden of processing full-resolution images while preserving localization precision
2Measurement precision
If the autonomous vehicle processes full-resolution images for localization, then the localization precision is improved, but the processing time increases
Solution Approach 1:
The system creates compressed embeddings that capture the essential visual information from full-resolution images. These embeddings retain enough detail for accurate localization while enabling much faster processing and comparison operations during real-time localization
Solution Approach 2:
Image embeddings are pre-computed and stored in advance, so that during localization the system only needs to retrieve and compare embeddings rather than processing full images, dramatically reducing processing time while maintaining precision
3Measurement precision
If the autonomous vehicle retrieves all candidate embeddings from the database, then the localization accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system segments the database of embeddings into discrete, manageable units that can be efficiently queried and retrieved. By organizing embeddings in a structured database with appropriate indexing, the system can retrieve only relevant candidate embeddings rather than processing the entire database, reducing computational complexity while maintaining accuracy
Solution Approach 2:
The system retrieves a limited set of candidate embeddings that are most likely to match the query image, based on preliminary filtering or indexing strategies. This partial retrieval approach provides sufficient accuracy for localization while avoiding the computational burden of retrieving and comparing all possible embeddings
Data Source
AI summary
Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.